Search Images Maps Play YouTube News Gmail Drive More »
Sign in
Screen reader users: click this link for accessible mode. Accessible mode has the same essential features but works better with your reader.

Patents

  1. Advanced Patent Search
Publication numberUS7716161 B2
Publication typeGrant
Application numberUS 10/314,427
Publication dateMay 11, 2010
Filing dateDec 6, 2002
Priority dateSep 24, 2002
Fee statusPaid
Also published asCA2499801A1, CN102096882A, EP1552360A2, EP1552360A4, US8171034, US20040059708, US20100174605, US20120173334, WO2004029758A2, WO2004029758A3
Publication number10314427, 314427, US 7716161 B2, US 7716161B2, US-B2-7716161, US7716161 B2, US7716161B2
InventorsJeffrey A. Dean, Georges R. Harik, Paul Bucheit
Original AssigneeGoogle, Inc,
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Methods and apparatus for serving relevant advertisements
US 7716161 B2
Abstract
The relevance of advertisements to a user's interests is improved. In one implementation, the content of a web page is analyzed to determine a list of one or more topics associated with that web page. An advertisement is considered to be relevant to that web page if it is associated with keywords belonging to the list of one or more topics. One or more of these relevant advertisements may be provided for rendering in conjunction with the web page or related web pages.
Images(6)
Previous page
Next page
Claims(34)
1. A computer implemented method for determining if an advertisement is relevant to a target document, the method comprising:
identifying targeting information for the advertisement;
identifying a set of one or more topics of the target document by analyzing the content of the target document;
comparing the targeting information to the set of one or more topics to determine if a match exists;
determining that the advertisement is relevant to the target document if the match exists;
making a serving determination using the determination of whether or not the advertisement is relevant to the target document; and
controlling serving of the advertisement for presentation to a requestor via a client device using the serving determination,
wherein the advertisement belongs to an advertiser,
wherein the targeting information includes a set of one or more topics previously provided from the advertiser,
wherein analyzing the content comprises identifying a set of one or more topics by calculating weighted terms for the target document based on text within the target document, and
wherein the weighted terms are provided in a term vector and are assigned weights based on the frequency with which the terms appear in the text of the target document.
2. A computer implemented method for determining if an advertisement is relevant to a target document, the method comprising:
identifying targeting information for the advertisement;
identifying a set of one or more topics of the target document by analyzing the content of the target document;
comparing the targeting information to the set of one or more topics to determine if a match exists;
determining that the advertisement is relevant to the target document if the match exists;
making a serving determination using the determination of whether or not the advertisement is relevant to the target document; and
controlling serving of the advertisement for presentation to a requestor via a client device using the serving determination,
wherein the advertisement belongs to an advertiser,
wherein the targeting information includes a set of one or more topics previously provided from the advertiser,
wherein analyzing the content comprises identifying a set of one or more topics by calculating weighted terms for the target document based on text within the target document, and
wherein the weighted terms are provided in a term vector and are assigned weights based on the infrequency with which the terms appear across a collection of documents.
3. A computer implemented method for determining if an advertisement is relevant to a target document, the method comprising:
identifying targeting information for the advertisement;
identifying a set of one or more topics of the target document by analyzing the content of the target document;
comparing the targeting information to the set of one or more topics to determine if a match exists;
determining that the advertisement is relevant to the target document if the match exists;
making a serving determination using the determination of whether or not the advertisement is relevant to the target document; and
controlling serving of the advertisement for presentation to a requestor via a client device using the serving determination,
wherein the advertisement belongs to an advertiser,
wherein the targeting information includes a set of one or more topics previously provided from the advertiser,
wherein analyzing the content comprises identifying a set of one or more topics by calculating weighted terms for the target document based on text within the target document, and
wherein the set of one or more topics contains those of the weighted terms whose weight exceeds a defined threshold.
4. The computer implemented method of claim 3, wherein identifying targeting information comprises generating a list of topics by analyzing content of the advertisement.
5. The computer implemented method of claim 3, wherein analyzing the content further comprises identifying a topic based on other portions of a collection of which the target document is a part, wherein the collection is one of (a) documents under a common directory, and (b) Web pages belonging to the same Website.
6. The computer implemented method of claim 3, wherein the act of identifying a set of one or more topics of the target document further comprises:
classifying the target document into a category; and
identifying a list of one or more topics of the target document based on the category.
7. The computer implemented method of claim 6, wherein meta-information associated with the target document is used to classify the target document into a category.
8. The computer implemented method of claim 7, wherein the meta-information includes information from another document that contains a reference to the target document.
9. The computer implemented method of claim 7, wherein the meta-information includes information from another document to which the target document refers.
10. The computer implemented method of claim 8, wherein the information from another document includes meta-information associated with the other document.
11. The computer implemented method of claim 3, wherein comparing the targeting information to the topic comprises comparing the targeting information to the topic or a related topic to determine if a match exists.
12. The computer implemented method of claim 3, wherein the target document is a target web page.
13. The computer implemented method of claim 12, wherein analyzing the content further comprises:
analyzing terms within the target web page and including them in the set of one or more topics if the frequency with which they appear in the target web page exceeds a threshold value.
14. The computer implemented method of claim 13, further comprising:
determining terms that are related to one or more topics in the set; and
supplementing the set to include the related terms.
15. The computer implemented method of claim 12, wherein analyzing the content further comprises analyzing terms within a title of the target web page and including them in the set of one or more topics if the frequency with which they appear in the title exceeds a threshold value.
16. The computer implemented method of claim 12, wherein analyzing the content further comprises:
determining at least one similar web page to the target web page;
revising the content of the target web page by supplementing it with the content of the similar web page; and
analyzing the revised content of the target web page to identify a set of one or more topics.
17. The computer implemented method of claim 16, wherein supplementing includes replacing at least a portion of the target web page content with at least a portion of the similar web page content.
18. The computer implemented method of claim 16, wherein determining at least one similar web page comprises determining that a web page is similar if it contains a link to the target web page.
19. The computer implemented method of claim 16, wherein determining at least one similar web page comprises determining that a web page is similar if the target web page contains a link to the web page.
20. The computer implemented method of claim 16, wherein the web page is contained in a host, and wherein determining at least one similar web page comprises determining that a web page is similar if it is contained within the same host as the target web page.
21. The computer implemented method of claim 16, wherein the web page is contained in a host, and wherein determining at least one similar web page comprises determining that a web page is similar if it is stored within a subdirectory of related pages on the same host as the target web page.
22. The computer implemented method of claim 12, wherein analyzing the content further comprises:
determining anchor text corresponding to the target web page;
revising the content of the target web page by supplementing it with the anchor text; and
analyzing the revised content of the target web page to identify a set of one or more topics.
23. The computer implemented method of claim 22, wherein supplementing includes replacing at least a portion of the target web page content with at least a portion of the anchor text.
24. The computer implemented method of claim 22, wherein supplementing includes replacing the target web page content with at least a portion of the anchor text.
25. The computer implemented method of claim 12, wherein analyzing the content further comprises:
classifying the target web page into a category; and
identifying a list of one or more topics of the target web page based on the category.
26. The computer implemented method of claim 25, wherein meta-information associated with the target web page is used to classify the target web page into a category.
27. The computer implemented method of claim 26, wherein the meta-information includes information from another document that contains a reference to the target web page.
28. The computer implemented method of claim 27, wherein the information from another document includes meta-information associated with the other document.
29. The computer implemented method of claim 26, wherein the meta-information includes anchor text corresponding to the target web page.
30. The computer implemented method of claim 12, wherein identifying targeting information comprises generating a set of one or more topics based on content of the advertisement.
31. The computer implemented method of claim 12, wherein identifying targeting information comprises generating a set of one or more topics for the advertisement based on text of queries on a search engine that yield a result that links to a web page on a web site to which the advertisement links.
32. The computer implemented method of claim 3, wherein the targeting information includes a set of one or more keywords or phrases previously provided from the advertiser through an ad campaign entry and management component.
33. The computer implemented method of claim 32, wherein the set of one or more keywords or phrases previously provided from the advertiser through an ad campaign entry and management component are associated with a separate advertising creative.
34. A computer implemented method for determining if an advertisement is relevant to a target document, the method comprising:
identifying targeting information for the advertisement;
identifying a set of one or more topics of the target document by analyzing the content of the target document;
comparing the targeting information to the set of one or more topics to determine if a match exists;
determining that the advertisement is relevant to the target document if the match exists;
making a serving determination using the determination of whether or not the advertisement is relevant to the target document; and
controlling serving of the advertisement for presentation to a requestor via a client device using the serving determination,
wherein the advertisement belongs to an advertiser,
wherein the targeting information includes a set of one or more topics previously provided from the advertiser,
wherein analyzing the content comprises identifying a set of one or more topics by calculating weighted terms for the target document based on text within the target document, and
wherein the set of one or more topics includes a predefined number of those of the weighted terms with the highest weights among the weighted terms.
Description
RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application Ser. No. 60/413,536, filed on Sep. 24, 2002, which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

A. Field of the Invention

The present invention relates generally to advertising and, more particularly, to serving relevant advertisements by comparing advertisers' targeting criteria to the content of media on which the advertisements are to be published.

B. Description of Related Art

Advertising using traditional media, such as television, radio, newspapers and magazines, is well known. Advertisers have used these types of media to reach a large audience with their advertisements (“ads”). To reach a more responsive audience, advertisers have used demographic studies. For example, advertisers may use broadcast events such as football games to advertise beer and action movies to a younger male audience. However, even with demographic studies and entirely reasonable assumptions about the typical audience of various media outlets, advertisers recognize that much of their ad budget is simply wasted because the target audience is not interested in the ad they are receiving.

Interactive media, such as the Internet, has the potential for better targeting of advertisements. For example, some websites provide an information search functionality that is based on query keywords entered by the user seeking information. This user query can be used as an indicator of the type of information of interest to the user. By comparing the user query to a list of keywords specified by an advertiser, it is possible to provide some form of targeted advertisements to these search service users. An example of such a system is the Adwords system offered by Google, Inc.

While systems such as Adwords have provided advertisers the ability to better target ads, their effectiveness is limited to sites where a user enters a search query to indicate their topic of interest. Most web pages, however, do not offer search functionality and for these pages it is difficult for advertisers to target their ads. As a result, often, the ads on non-search pages are of little value to the viewer of the page and are therefore viewed more as an annoyance than a source of useful information. Not surprisingly, these ads typically provide the advertiser with a lower return on investment than search-based ads, which are more targeted.

It would be useful, therefore, to have methods and apparatus for providing relevant ads for situations where a document is provided to an end user, but not in response to an express indication of a topic of interest by the end user (e.g., not responsive to the end user submitting a search query).

SUMMARY OF THE INVENTION

Systems and methods consistent with the present invention address this and other needs by identifying targeting information for an advertisement, analyzing the content of a target document to identify a list of one or more topics for the target document, comparing the targeting information to the list of topics to determine if a match exists, and determining that the advertisement is relevant to the target document if the match exists.

Additional aspects of the present invention are directed to computer systems and to computer-readable media having features relating to the foregoing aspects.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, explain the invention. In the drawings,

FIG. 1 is a diagram illustrating an environment within which the invention may be implemented;

FIG. 2 is a diagram functionally illustrating an advertising system consistent with the invention;

FIG. 3 is a diagram illustrating apparatus with which the invention may be implemented;

FIG. 4 is a flow diagram of an exemplary method for providing relevant advertisements, consistent with the present invention; and

FIG. 5 is a sample target document.

DETAILED DESCRIPTION

The following detailed description of the invention refers to the accompanying drawings. The detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims and equivalents.

The present invention involves methods and apparatus for determining advertisements that are relevant to a given document. In one implementation, the document is a web page and the advertisements are electronic files that are capable of being rendered on that web page. A set, such as a list, of topics corresponding to the web page is generated by analyzing the content of the web page. There are a variety of techniques by which this may be performed, one of which is by computing a term vector for the web page and selecting the top N terms from that vector. The list of topics is compared to target information associated with the advertisements (e.g., keywords specified for the advertisements) to determine which of the advertisements are relevant to the web page. Some or all of these relevant advertisements may then be associated with the web page so that they may be rendered (e.g., displayed) with the web page.

Those skilled in the art will recognize that many other implementations are possible, consistent with the present invention.

A. Environment and Architecture

FIG. 1 is a diagram illustrating an environment within which the invention may be implemented. The environment includes an advertiser 110, an advertising system 120, an advertisement consumer 130, and an advertising target 140.

Advertiser 110 may be the party that directly sells the goods or services being advertised (e.g., Amazon.com) or an agent authorized to act on the advertiser's behalf. The advertisement desired by advertiser 110 may exist in a variety of forms ranging from standard print advertisements, online advertisements, audio advertisements, audio/visual advertisements, or any other type of sensory message desired.

Advertising system 120 interfaces with both the advertiser 110 and the advertisement consumer 130. It may perform a variety of functions, as explained in more detail below in reference to FIG. 2. This invention may be used with such an advertising system 120.

Advertisement consumer 130 is the entity that will issue a request for advertisements to advertising system 120, obtain the advertisements from advertising system 120, and present the advertisement to the advertising target 140. Typically, the advertisement consumer is the entity that provides the content with which the advertisement is to be associated. In one implementation, the advertising consumer 130 is a search engine, such as that employed by Google, Inc. at www.google.com.

Advertising target 140 is the individual (or set of individuals) who ultimately receive the advertisement. In the case of visual advertisements, for example, the advertisement target 140 is the person who views the advertisement.

FIG. 2 is a diagram functionally illustrating an advertising system consistent with the invention. The system includes an ad campaign entry and management component 210, a tools component 220, a billing component 230, one or more databases 240, an ad consumer interface component 250, an ad selection component 260, an ad ordering component 270, an ad serving component 280, and a statistics engine component 290. If the present invention is to be used with such an advertising system, it will primarily concern ad selection component 260. To help understand the invention, other components of the advertising system will be explained below. Furthermore, although FIG. 2 shows a particular arrangement of components constituting advertisement system 120, those skilled in the art will recognize that not all components need be arranged as shown, not all components are required, and that other components may be added to, or replace, those shown.

Ad entry and management component 210 is the component by which the advertiser enters information required for an advertising campaign and manages the campaign. An ad campaign contains one or more advertisements that are related in some manner. For example, the Ford Motor Company may have an ad campaign for zero percent financing, which could contain a series of advertisements related to that topic. Among the other things that could be provided by an advertiser through ad entry and management component 210 are the following: one or more advertising creatives (simply referred to as “ads” or “advertisements”), one or more set of keywords or topics associated with those creatives (which may be used as targeting information for the ads), geographic targeting information, a value indication for the advertisement, start date, end date, etc. The data required for, or obtained by, ad entry and management component 210 resides in one of the databases 240.

Tools component 220 contains a variety of tools designed to help the advertiser 110 create, monitor, and manage its campaigns. For example, tools component 220 may contain a tool for helping advertiser 110 estimate the number of impressions an ad will receive for a particular keyword or topic. Similarly, tools component 220 may be used to help advertiser 110 generate a list of keywords or topics for a given advertisement, or to generate additional keywords or topics based on representative ones supplied by advertiser 110. Other possible tools may be provided as well. Depending on the nature of the tool, one or more databases 240 may be used to gather or store information.

Billing component 230 helps perform billing-related functions. For example, billing component 230 generates invoices for a particular advertiser 110 or ad campaign. In addition, billing component 230 may be used by advertiser 110 to monitor the amount being expended for its various campaigns. The data required for, or obtained by, billing component 230 resides in a database 240.

Databases 240 contain a variety of data used by advertising system 120. In addition to the information mentioned above in reference to ad entry and management system 210, databases 240 may contain statistical information about what ads have been shown, how often they have been shown, the number of times they have been selected, who has selected those ads, how often display of the ad has led to consummation of a transaction, etc. Although the databases 240 are shown in FIG. 2 as one unit, one of ordinary skill in the art will recognize that multiple databases may be employed for gathering and storing information used in advertising system 120.

Ad consumer interface 250 is a component that interfaces with ad consumer 130 to obtain or send information. For example, ad consumer 130 may send a request for one or more advertisements to ad consumer interface 250. The request may include information such as the site requesting the advertisement, any information available to aid in selecting the advertisement, the number of ads requested, etc. In response, ad consumer interface 250 may provide one or more advertisements to ad consumer 130. In addition, ad consumer 130 may send information about the performance of the advertisement back to the ad system via the ad consumer interface 250. This may include, for example, the statistical information described above in reference to a database 240. The data required for, or obtained by, ad consumer interface component 250 resides in a database 240.

Ad selection component 260 receives a request for a specified number of advertisements, coupled with information to help select the appropriate advertisements. This information may include, for example, a search query specified by an end user. Alternatively, or in addition, as described in more detail below, this information may include data related to the content of the page for which the advertisements are being requested.

Ad ordering component 270 receives a list of relevant ads from ad selection component 260 and determines a preference order in which they should be rendered to an end user. For example, relevant ads may be ordered based on the value indication associated with each ad. These ordered ads may be provided to an ad serving component 280.

Ad serving component 280 receives an ordered list of ads from ad ordering component 270, and formats that list into a manner suitable for presenting to ad consumer 130. This may involve, for example, rendering the ads into hypertext markup language (HTML), into a proprietary data format, etc.

Statistics engine 290 contains information pertaining to the selection and performance of advertisements. For example, statistics engine 290 may log the information provided by ad consumer 130 as part of an ad request, the ads selected for that request by ad selection component 260, the order selected by ad ordering component 270, and the presentation of the ads by ad serving component 280. In addition, statistics engine 290 may log information about what happens with the advertisement once it has been provided to ad consumer 130. This includes information such as on what location the ad was provided, what the response was to the advertisement, what the effect was of the advertisement, etc.

FIG. 3 is a diagram illustrating an architecture in which the present invention may be implemented. The architecture includes multiple client devices 302, a server device 310, and a network 301, which may be, for example, the Internet. Client devices 302 each include a computer-readable medium 309, such as random access memory, coupled to a processor 308. Processor 308 executes program instructions stored in memory 309. Client devices 302 may also include a number of additional external or internal devices, such as, without limitation, a mouse, a CD-ROM, a keyboard, and a display. Thus, as will be appreciated by those skilled in the art, the client devices may be personal computers, personal digital assistances, mobile phones, content players, etc.

Through client devices 302, requesters 305 can communicate over network 301 with each other and with other systems and devices coupled to network 301, such as server device 310. Requestors 305 may, for example, be advertisers 110, advertisement consumer 130, or advertising target 140.

Similar to client devices 302, server device 310 may include a processor 311 coupled to a computer readable memory 312. Server device 310 may additionally include a secondary storage element, such as a database 240.

Client processors 308 and server processor 311 can be any of a number of well known micro-processors, such as processors from Intel Corporation, of Santa Clara, California. In general, client device 302 may be any type of computing platform connected to a network and that interacts with application programs, such as a digital assistant or a “smart” cellular telephone or pager. Server 310, although depicted as a single computer system, may be implemented as a network of computer processors.

Memory 312 may contain a number of programs, such as the components described above in reference to FIG. 2.

B. Operation

FIG. 4 is a flow diagram of an exemplary method for determining if an advertisement is relevant to a document, consistent with the present invention. As used herein, the term “document” includes any type of paper or electronic document or file, including audio, video, image, text, etc. That is, as will be appreciated by one skilled in the art, a “document” as used in the specification is any machine-readable and machine-storable work product. A document may be a file, a combination of files, one or more files with embedded links to other files, etc. For the sake of illustration, it may be understood that the process described herein takes place as part of the ad selection component 260, although those skilled in the art will recognize that it need not take place in that component alone.

The exemplary method is not limited by the order shown in the flow diagram. The process identifies targeting information for an advertisement. (Stage 410). The targeting information may be in the form of a list of keywords or phrases associated with the advertisement (e.g., “honda”, “honda cars”, “cars”, etc.), as provided by advertiser 110 through ad campaign entry and management component 210. Alternatively, or in addition, the targeting information may be determined algorithmically, based on the content of the advertisement, the goods or services being advertised, the targeting of other related advertisements, etc. For example, if the content of the advertisement includes “Buy honda cars at the lowest prices of the year!”, the terms “honda” or “honda cars” may be extracted from that content. The targeting information may also include other demographic information, such as geographic location, affluence, etc. Thus, the targeting information is simply some information from which a topic may be derived.

Next, the target document (i.e., the document corresponding to which a relevant advertisement is requested) is analyzed to identify a topic corresponding to that target document. (Stage 420). The target document may be stored on a database 240 or may be provided by ad consumer 130 via ad consumer interface component 250. There are numerous ways in which the target document may be analyzed to identify this topic, as described below in reference to FIG. 5 and related text.

The targeting information identified in stage 410 is compared to the one or more topics identified in stage 420 to determine if a match exists. (Stage 430). A “match” need not be an exact match. Instead, a match is an indication of a relatively high degree of similarly, and/or a predetermined (e.g., absolute) degree of similarity. If a match exists, the advertisement is determined to be relevant to the target document (stage 440) and may be provided to ad ordering component 270, for eventual provision to ad consumer 130 via ad consumer interface component 250.

Those skilled in the art will also recognize that the functions described in each stage are illustrative only, and are not intended to be limiting.

One way to identify a topic corresponding to the target document is by analyzing some or all text within the target document, which shall be illustrated in reference to FIG. 5. FIG. 5 shows a sample document, entitled “Travels in Italy”, which contains a collection of travel-related information pertaining to Italy. The document text contains the term “restaurant” (appearing 20 times), “chianti” (appearing 10 times), and “the” (appearing 100 times). It could be determined that one or more of each term (word or phrase) that appears in the title of the target document corresponds to a topic of the target document. On this basis, the topics for this document may be “travels”, “in”, and/or “italy.”

Alternatively, it could be determined that one or more of each term that appears in the body of the target document corresponds to a topic of the target document. In the simplest case, each term within the target document would be identified as a topic. A slightly more complex approach would be to identify a term as a topic if it appears in the target document more than N times, such as N=2 (and indeed such a threshold-based approach could be used whenever terms within text are being analyzed). Even more complex analysis could be performed, such as by using a term vector for the target document, which assigns weights to each term. For example, terms that appear frequently in the target document may be assigned a relatively higher weight than those that appear less frequently. And so the term “the” would have a higher weight than “restaurant”, which would have a higher weight than “chianti”.

In addition, the weighting could be adjusted to give higher weight to terms that appear less frequently in a collection, such as a collection to which the document belongs or the general collection of documents. For example, the term “chianti” does not appear very commonly across the general collection of documents and so its weight may be boosted. Conversely, the term “the” appears so frequently across a collection of documents that its weight may be reduced or eliminated altogether.

In any situation where terms within text are assigned weights or scores, those resulting scores may be used to determine which terms will be identified as topics for the target document. For example, it may be determined that only the top scoring term would constitute a topic for the target document. Alternatively, or in addition, it may be determined that the top Z terms (or a subset thereof) will constitute topics for the target document, with Z being some defined number. Alternatively, or in addition, it may be determined that terms having a score that exceeds Y (or a subset thereof) will constitute topics for the target document, with Y being some defined number. Thus, as one skilled in the art will appreciate, topics may be determined based on absolute and/or relative criteria.

Alternatively, or in addition to using text or other information within the target document, meta-information associated with the target document may be used. For example, a reference to the target document by another document may contain a brief description of the target document. Assume a document called “Entertainment” that contains a reference to the target document and describes it as “For a description of restaurants and wine in Italy, see ‘Travels in Italy’.” In the context of a web page, this is often described as anchor text. One or more such brief descriptions may be used to revise (figuratively) the target document by supplementing or replacing some or all of its content with the brief descriptions. So, for example, the topic could be identified from the combination of the target document's title and the brief descriptions of the target document.

Alternatively, or in addition to the brief descriptions from these references, the references themselves may be used. For example, a reference from another document to the target document may be used as an indication that the two documents are similar. Alternatively, or in addition, a reference from the target document to another document may be used as an indication that the two documents are similar. So a reference between the “Entertainment” document and the “Travels in Italy” document may indicate that the two are related. In the context of web pages, these references occur in the form of links from one web page to another. On this basis, the content (or meta-information) of the other document may be used to revise (figuratively) the target document by supplementing or replacing its content with that of the other document. The revised target document's content may then be analyzed using the techniques described above to identify one or more topics.

Alternatively, or addition to using the content (including perhaps metadata) associated with a target document, other techniques may be used to identify one or more topics for the target document. For example, the top N queries (or subset thereof) that result in a reference to the target document could be determined to constitute a topic for the target document, with N being some defined number. These may be, for example, text queries in a search engine that yield a result that links to the target document or web page. Alternatively, or in addition, the content of other similar documents (e.g., in the same collection as the target document, in the same category as the target document, etc.) may be used to revise (figuratively) the content of the target document. Any of the techniques described above may then be used to analyze the target document to identify one or more topics. In the context of web pages, this may be other web pages that are stored within a subdirectory of related pages on the same host as the target web page. Alternatively, or in addition, any technique for classifying the target document into a set of one or more topics or categories may be used. Even the search query history of one or more users who visit the target document (or target web page) may be used to identify a topic for the target document or web page, on the theory that a visit to the target document that is temporally proximate to that search query history indicates that the user thought the concepts were related. For example, if a user searched for “italian wine” and then soon afterwards visited the “Travels in Italy” document, the content of that prior search could be used to determine that “italian” and/or “wine” are potential topics for the “Travels in Italy” document.

Using one or more of the various techniques described above, or other techniques, one or more topics may be identified for the target document. Once these topics have been identified, a variety of techniques may be used to determine other topics that are related to those identified topics. For example, a thesaurus could be used to determine other topics (e.g., synonyms) that are closely related to the identified topics or that are conceptually similar to the identified topics.

For the sake of clarity, the foregoing references to “revising” the target document are a figurative aid in understanding the use of additional information that is not literally within the target document. Those skilled in the art will recognize that the target document need not be actually revised to make use of this additional information.

C. Conclusion

The foregoing description of preferred embodiments of the present invention provides illustration and description, but is not intended to be exhaustive or to limit the invention to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the invention.

The scope of the invention is defined by the claims and their equivalents.

Patent Citations
Cited PatentFiling datePublication dateApplicantTitle
US5724521 *Nov 3, 1994Mar 3, 1998Intel CorporationMethod and apparatus for providing electronic advertisements to end users in a consumer best-fit pricing manner
US5740549Jun 12, 1995Apr 14, 1998Pointcast, Inc.Information and advertising distribution system and method
US5754938 *Oct 31, 1995May 19, 1998Herz; Frederick S. M.Pseudonymous server for system for customized electronic identification of desirable objects
US5794050Oct 2, 1997Aug 11, 1998Intelligent Text Processing, Inc.Method for interpreting natural language input using a computer system
US5809242Apr 19, 1996Sep 15, 1998Juno Online Services, L.P.Electronic mail system for displaying advertisement at local computer received from remote system while the local computer is off-line the remote system
US5815830Dec 18, 1995Sep 29, 1998Anthony; Andre CharlesFor searching similar strings for associating portions of stored text
US5848397Apr 19, 1996Dec 8, 1998Juno Online Services, L.P.Method and apparatus for scheduling the presentation of messages to computer users
US5848407May 22, 1997Dec 8, 1998Matsushita Electric Industrial Co., Ltd.Hypertext document retrieving apparatus for retrieving hypertext documents relating to each other
US5887133 *Jan 15, 1997Mar 23, 1999Health Hero NetworkSystem and method for modifying documents sent over a communications network
US5948061Oct 29, 1996Sep 7, 1999Double Click, Inc.Method of delivery, targeting, and measuring advertising over networks
US6026368Jul 17, 1995Feb 15, 200024/7 Media, Inc.On-line interactive system and method for providing content and advertising information to a targeted set of viewers
US6041323Apr 17, 1997Mar 21, 2000International Business Machines CorporationInformation search method, information search device, and storage medium for storing an information search program
US6044376Apr 24, 1997Mar 28, 2000Imgis, Inc.Content stream analysis
US6078914Dec 9, 1996Jun 20, 2000Open Text CorporationNatural language meta-search system and method
US6134532 *Nov 14, 1997Oct 17, 2000Aptex Software, Inc.System and method for optimal adaptive matching of users to most relevant entity and information in real-time
US6144944Apr 22, 1998Nov 7, 2000Imgis, Inc.Computer system for efficiently selecting and providing information
US6167382Jun 1, 1998Dec 26, 2000F.A.C. Services Group, L.P.Design and production of print advertising and commercial display materials over the Internet
US6269361May 28, 1999Jul 31, 2001Goto.ComSystem and method for influencing a position on a search result list generated by a computer network search engine
US6285999Jan 9, 1998Sep 4, 2001The Board Of Trustees Of The Leland Stanford Junior UniversityMethod for node ranking in a linked database
US6308202 *Sep 8, 1998Oct 23, 2001Webtv Networks, Inc.System for targeting information to specific users on a computer network
US6327574 *Feb 1, 1999Dec 4, 2001Encirq CorporationHierarchical models of consumer attributes for targeting content in a privacy-preserving manner
US6356898 *Nov 13, 1998Mar 12, 2002International Business Machines CorporationMethod and system for summarizing topics of documents browsed by a user
US6360221Sep 21, 1999Mar 19, 2002Neostar, Inc.Method and apparatus for the production, delivery, and receipt of enhanced e-mail
US6366298 *Jun 3, 1999Apr 2, 2002Netzero, Inc.Monitoring of individual internet usage
US6385592 *Jun 30, 1999May 7, 2002Big Media, Inc.System and method for delivering customized advertisements within interactive communication systems
US6401075Feb 14, 2000Jun 4, 2002Global Network, Inc.Methods of placing, purchasing and monitoring internet advertising
US6449657Aug 6, 1999Sep 10, 2002Namezero.Com, Inc.Internet hosting system
US6516321Aug 13, 1999Feb 4, 2003Carlos De La HuergaMethod for database address specification
US6665656 *Oct 5, 1999Dec 16, 2003Motorola, Inc.Method and apparatus for evaluating documents with correlating information
US6665838 *Jul 30, 1999Dec 16, 2003International Business Machines CorporationWeb page thumbnails and user configured complementary information provided from a server
US6681223 *Jul 27, 2000Jan 20, 2004International Business Machines CorporationSystem and method of performing profile matching with a structured document
US6772200Dec 30, 1997Aug 3, 2004Intel CorporationSystem for providing non-intrusive dynamic content to a client device
US6804659Jan 14, 2000Oct 12, 2004Ricoh Company Ltd.Content based web advertising
US6985882Feb 5, 1999Jan 10, 2006Directrep, LlcMethod and system for selling and purchasing media advertising over a distributed communication network
US7013298May 3, 1999Mar 14, 2006Hyperphrase Technologies, LlcMethod and system for automated data storage and retrieval
US7039599Jun 15, 1998May 2, 2006Doubleclick Inc.Method and apparatus for automatic placement of advertising
US7136875Feb 26, 2003Nov 14, 2006Google, Inc.Serving advertisements based on content
US7139732Jul 24, 2000Nov 21, 2006Roger Marx DesenbergSystems, methods, and computer program products facilitating real-time transactions through the purchase of lead options
US20020123912 *Oct 31, 2001Sep 5, 2002ContextwebInternet contextual communication system
US20040073485 *Apr 10, 2002Apr 15, 2004Informlink, Inc.Method for an on-line promotion server
US20040249709Aug 25, 2003Dec 9, 2004Donovan Kevin RjbMethod and system for dynamic textual ad distribution via email
EP0913779A2May 14, 1998May 6, 1999Mitsubishi Denki Kabushiki KaishaBrowser for documents with annotations
JP2001243256A Title not available
JP2001307464A Title not available
JP2002108924A Title not available
JP2002117049A Title not available
JP2002245061A Title not available
JP2002259790A Title not available
JPH1145278A Title not available
JPH09311870A Title not available
KR100295032B1 Title not available
KR20010074095A Title not available
KR20010082984A Title not available
KR20010084925A Title not available
KR20020032774A Title not available
KR20020067828A Title not available
WO1997021183A1Dec 6, 1996Jun 12, 1997Bell Communications ResMethod and system for placing advertisements in a computer network
WO2001044992A1 *Oct 31, 2000Jun 21, 2001Yellowbrix IncContext matching system and method
WO2001050320A1Dec 21, 2000Jul 12, 2001Auctionwatch Com IncMinimal impact crawler
WO2001063472A2Feb 22, 2001Aug 30, 2001David Tzvi BenSystem and method for secure, query-driven, targeted electronic solicitation
Non-Patent Citations
Reference
1AdForce, Inc., A Complete Guide to AdForce, Version 2.6, 1998.
2AdForce, Inc., S-1/A SEC Filing, May 6, 1999.
3AdKnowledge Campaign Manager: Reviewer's Guide, AdKnowledge, Aug. 1998.
4AdKnowledge Market Match Planner: Reviewer's Guide, AdKnowledge, May 1998.
5Ad-Star.com website archive from www. Archive.org, Apr. 12, 1997 and Feb. 1, 1997.
6 *Automating Ad Intelligence by Jayson Matthews, Silicon Valley Internet.com, (http://siliconvalley.internet.com/news/article.php/3531 420651), (Published Jul. 24, 2000).
7Baseview Products, Inc., AdManagerPro Administration Manual v. 2.0, Dec. 1998.
8Baseview Products, Inc., ClassManagerPro Administration Manual v. 1.0.5, Feb. 1, 1997.
9Business Wire, "Global Network, Inc. Enters Into Agreement in Principle With Major Advertising Agency," Oct. 4, 1999.
10 *C. Aggarwal, J. Wolf and P. Yu, "A Framework for the Optimizing of WWW Advertising", International IFIP Working Conference on Electronic Commerce, Hamburg, Germany, 1998, published in Trends in Distributed Systems for Electronic Commerce, edited by W. Lamersdorf and M. Merz, Springer Verlag Lecture Notes in Computer Science, vol. 1402, pp. 1 10, 1998.
11Canadian Office Action to Canadian Patent Application No. 2,499,669, dated Jun. 3, 2008 (3 pgs.).
12Canadian Office Action to Canadian Patent Application No. 2,499,801, dated Jun. 4, 2009 (3 pgs.).
13Chinese Office Action for Chinese Patent Application No. 038227495, dated Nov. 9, 2007 (8 pgs.) (with translation 21 pgs.).
14Communication for European Patent Application No. 03 759 523.8 - 1238, mailed on Feb. 23, 2010 (4 pgs.).
15Decision Rendered by Trial Board for Invalidation Trial Against Korean Patent No. 797,401 (62 pgs.) (with English summary (1 pg.)) (Filed on Apr. 28, 2009.).
16Dedrick, R., A Consumption Model for Targeted Electronic Advertising, Intel Architecture Labs, IEEE, 1995.
17Dedrick, R., Interactive Electronic Advertising, IEEE, 1994.
18First Reply Brief for Invalidation Trial Against Korean Patent No. 797,401 (41 pgs.) (with summarized English translation (19 pgs.)) (Filed on Jul. 22, 2008.).
19Fourth Reply Brief for Invalidation Trial Against Korean Patent No. 797,401 (17 pgs.) (with summarized English translation (7 pgs.)) (Filed on Apr. 10, 2009.).
20Graham, I., The HTML Sourcebook, pp. iii-viii; 22-27; 87-88; 166-168; 409-416, John Wiley & Sons (1995) (ISBN 0471118494).
21Information Access Technologies, Inc., Aaddzz brochure, "The Best Way To Buy and Sell Web Advertising Space," © 1997.
22Information Access Technologies, Inc., Aaddzz.com website archive from www. Archive.org, archived on Jan. 30, 1998.
23 *Kohda Youji et al., "Ubiquitous Advertising on the WWW: Merging Advertisement on the Browser", Fifth International World Wide Conference, May 6-10, 1996, Paris, France, pp. 1-9.
24 *Marc Langheinrich et al., "Unintrusive Customization Techniques for Web Advertising", Computer Networks, vol. 31. No. 11 16, pp. 1259 1272. (Published 1999).
25 *Mark Rosenstein and Carol Lochbaum, "Recommending from Content: Preliminary Results from an E Commerce Experiment", CHI 2000. Published Apr. 1 6, 2000, The Hague, The Netherlands.
26Mills, T., "Providing World Wide Access to Historical Sources," Computer Networks and ISDN Systems, vol. 29, Nos. 8-13, pp. 1317-1325, (Sep. 1997) (Presented at Sixth Int'l World Wide Web Conf., Santa Clara, CA (Apr. 7-11, 1997)).
27Myka, A., "Automatic Hypertext Conversion of Paper Document Collections," (ch. 6), Digital Libraries Workshop, pp. 65-90, DL '94, (Newark NJ) (May, 1994) (selected papers).
28Notice of Preliminary Rejection to Korean Patent Application No. 10-2005-7005101, dated Sep. 14, 2006, (4 pgs.), with English language translation (5 pgs.).
29Notice of Rejection Grounds for Japanese Patent Application No. 2005-501981, mailed Nov. 6, 2007 (3 pgs.) with translation (3 pgs.).
30Petitioner's Appeal Brief for Invalidation Trial Against Korean Patent No. 797,401 (51 pgs.) (with summarized English translation (20 pgs.)) (Filed on Jul. 17, 2009.).
31Petitioner's First Brief Against Korean Patent No. 797,401 (37 pgs.) (with summarized English translation (10 pgs.)) (Filed on Jun. 11, 2008.).
32Petitioner's Fourth Brief Against Korean Patent No. 797,401 (7 pgs.) (with summarized English translation (3 pgs.)) (Filed on Mar. 17, 2009.).
33Petitioner's Second Brief Against Korean Patent No. 797,401 (34 pgs.) (with summarized English translation (11 pgs.)) (Filed on Dec. 1, 2008.).
34Petitioner's Second Brief for Invalidation Trial Against Korean Patent No. 797,401 (29 pgs.) (with summarized English translation (10 pgs.)) (Filed on Jan. 18, 2010.).
35Petitioner's Third Brief Against Korean Patent No. 797,401 (21 pgs.) (with summarized English translation (8 pgs.)) (Filed on Feb. 9, 2009.).
36Request for Reexamination of U.S. Patent No. 7,249,059 B2, Control No. 95/001,069.
37Respondent's First Reply Brief for Invalidation Trial Against Korean Patent No. 797,401 (38 pgs.) (with summarized English translation (20 pgs.)) (Filed on Nov. 6, 2009.).
38Second Reply Brief for Invalidation Trial Against Korean Patent No. 797,401 with Request for Amendment (80 pgs.) (with summarized English translation (29 pgs.)) (Filed on Jan. 14, 2009.).
39Tenenbaum, et al., "Eco System: An Internet Commerce Architecture," IEEE Computer, pp. 48-55, (May 1997).
40Third Reply Brief for Invalidation Trial Against Korean Patent No. 797,401 (43 pgs.) (with summarized English translation (16 pgs.)) (Filed on Feb. 25, 2009.).
41Thistlewaite, P., "Automatic Construction and Management of Large Open Webs," Information Processing and Management; an International Journal (Ed. Tefko Saracevic), vol. 33, No. 2, pp. 161-173 (Mar. 1997) (ISSN 0306-4573).
42U. S. Appl. No. 95/001,061, Reexamination of Stone et al.
43U. S. Appl. No. 95/001,068, Reexamination of Stone et al.
44U. S. Appl. No. 95/001,069, Reexamination of Stone et al.
45U. S. Appl. No. 95/001,073, Reexamination of Stone et al.
Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US8060504Aug 12, 2010Nov 15, 2011Jericho Systems CorporationMethod and system for selecting content items to be presented to a viewer
US8200530May 21, 2008Jun 12, 2012Google Inc.Determining complementary product content from primary product document information and providing such complementary product content in association with the primary product document, in an online environment
US8200531Nov 23, 2010Jun 12, 2012Google Inc.Determining complementary product content from primary product document information and providing such complementary product content in association with the primary product document, in an online environment
US8301649Sep 4, 2008Oct 30, 2012Amazon Technologies, Inc.Dynamic categorizations for electronic advertising
US8312001 *Feb 25, 2011Nov 13, 2012Google Inc.Determining content to be provided by an entity for rendering via a resource in a target document or notifying an alternative entity of the availability of the resource
US8392395Sep 3, 2010Mar 5, 2013News Distribution Network, Inc.Determining advertising placement on preprocessed content
US8463772May 13, 2010Jun 11, 2013Google Inc.Varied-importance proximity values
US8666816Sep 14, 2004Mar 4, 2014Google Inc.Method and system for access point customization
US8700630 *Feb 24, 2009Apr 15, 2014Yahoo! Inc.Algorithmically generated topic pages with interactive advertisements
US8805727May 31, 2012Aug 12, 2014Google Inc.Determining complementary product content from primary product document information and providing such complementary product content in association with the primary product document, in an online environment
US8832129Sep 15, 2012Sep 9, 2014Amazon Technologies, Inc.Dynamic categorizations for electronic advertising
US20090164287 *Dec 24, 2007Jun 25, 2009Kies Jonathan KMethod and apparatus for optimizing presentation of media content on a wireless device based on user behavior
US20110087545 *Dec 20, 2010Apr 14, 2011John Nicholas GrossSystem & Method of Delivering Content Based Advertising Within a Blog
US20110270677 *Feb 25, 2011Nov 3, 2011Bronson Kenneth WDetermining content to be provided by an entity for rendering via a resource in a target document or notifying an alternative entity of the availability of the resource
US20110282964 *May 13, 2010Nov 17, 2011Qualcomm IncorporatedDelivery of targeted content related to a learned and predicted future behavior based on spatial, temporal, and user attributes and behavioral constraints
US20120102020 *Jan 3, 2012Apr 26, 2012Mark PearsonGenerating Search Result Listing with Anchor Text Based Description of Website Corresponding to Search Result
US20120123876 *Nov 17, 2010May 17, 2012Sreenivasa Prasad SistaRecommending and presenting advertisements on display pages over networks of communication devices and computers
WO2011143625A2 *May 13, 2011Nov 17, 2011Qualcomm IncorporatedDelivery of targeted content related to a learned and predicted future behavior based on spatial, temporal, and user attributes and behavioral constraints
Classifications
U.S. Classification705/14.49, 709/217, 707/944, 705/14.54, 707/731
International ClassificationG06Q30/00, G06F7/00, G06F, G06F17/30
Cooperative ClassificationY10S707/944, G06Q30/0251, G06Q30/02, G06Q30/0256
European ClassificationG06Q30/02, G06Q30/0256, G06Q30/0251
Legal Events
DateCodeEventDescription
Sep 4, 2014ASAssignment
Owner name: GOOGLE INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DEAN, JEFFREY A.;HARIK, GEORGES;BUCHHEIT, PAUL;SIGNING DATES FROM 20050614 TO 20050823;REEL/FRAME:033686/0710
Nov 12, 2013FPAYFee payment
Year of fee payment: 4
Mar 11, 2003ASAssignment
Owner name: GOOGLE, INC., CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DEAN, JEFFREY A.;HARIK, GEORGES R.;BUCHEIT, PAUL;REEL/FRAME:013870/0796
Effective date: 20030224
Owner name: GOOGLE, INC.,CALIFORNIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DEAN, JEFFREY A.;HARIK, GEORGES R.;BUCHEIT, PAUL;US-ASSIGNMENT DATABASE UPDATED:20100511;REEL/FRAME:13870/796